Overview

Dataset statistics

Number of variables43
Number of observations847102
Missing cells96
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 GiB
Average record size in memory1.3 KiB

Variable types

CAT22
NUM16
UNSUPPORTED3
DATE2

Warnings

QU has constant value "847102" Constant
FOLIO has a high cardinality: 377431 distinct values High cardinality
SUB has a high cardinality: 8814 distinct values High cardinality
SITE_ADDR has a high cardinality: 376952 distinct values High cardinality
SITE_CITY has a high cardinality: 66 distinct values High cardinality
SITE_ZIP has a high cardinality: 2220 distinct values High cardinality
SD1 has a high cardinality: 169 distinct values High cardinality
NBHC has a high cardinality: 313 distinct values High cardinality
BLOCK_NUM has a high cardinality: 887 distinct values High cardinality
LOT_NUM has a high cardinality: 19649 distinct values High cardinality
BLDG is highly correlated with JUST and 1 other fieldsHigh correlation
JUST is highly correlated with BLDG and 2 other fieldsHigh correlation
ASD_VAL is highly correlated with JUST and 2 other fieldsHigh correlation
TAX_VAL is highly correlated with JUST and 1 other fieldsHigh correlation
REGION is highly correlated with MARKET_AREA_CDHigh correlation
MARKET_AREA_CD is highly correlated with REGIONHigh correlation
S_AMT is highly skewed (γ1 = 21.81267624) Skewed
ACREAGE is highly skewed (γ1 = 41.55003936) Skewed
FOLIO is uniformly distributed Uniform
SITE_ADDR is uniformly distributed Uniform
df_index has unique values Unique
ACT is an unsupported type, check if it needs cleaning or further analysis Unsupported
EFF is an unsupported type, check if it needs cleaning or further analysis Unsupported
BASE is an unsupported type, check if it needs cleaning or further analysis Unsupported
tBLDGS has 168348 (19.9%) zeros Zeros
EXF has 316973 (37.4%) zeros Zeros
AGE has 177637 (21.0%) zeros Zeros

Reproduction

Analysis started2022-05-27 21:55:34.417352
Analysis finished2022-05-27 21:59:43.427647
Duration4 minutes and 9.01 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct847102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean973128.6992
Minimum8
Maximum2047227
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:43.674010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile91386.05
Q1442455
median963574
Q31480502.75
95-th percentile1944098.9
Maximum2047227
Range2047219
Interquartile range (IQR)1038047.75

Descriptive statistics

Standard deviation590885.1658
Coefficient of variation (CV)0.6072014589
Kurtosis-1.180949512
Mean973128.6992
Median Absolute Deviation (MAD)519329.5
Skewness0.09236690488
Sum8.243392673e+11
Variance3.491452792e+11
MonotocityStrictly increasing
2022-05-27T17:59:43.784529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81< 0.1%
 
12901311< 0.1%
 
12900931< 0.1%
 
12900951< 0.1%
 
12901001< 0.1%
 
12901011< 0.1%
 
12901071< 0.1%
 
12901081< 0.1%
 
12901091< 0.1%
 
12901151< 0.1%
 
12901261< 0.1%
 
12901301< 0.1%
 
12901351< 0.1%
 
12900901< 0.1%
 
12901371< 0.1%
 
12901381< 0.1%
 
12901401< 0.1%
 
12901421< 0.1%
 
12901431< 0.1%
 
12901521< 0.1%
 
12901551< 0.1%
 
12901571< 0.1%
 
12901581< 0.1%
 
12901591< 0.1%
 
12900911< 0.1%
 
Other values (847077)847077> 99.9%
 
ValueCountFrequency (%) 
81< 0.1%
 
91< 0.1%
 
111< 0.1%
 
141< 0.1%
 
201< 0.1%
 
211< 0.1%
 
221< 0.1%
 
231< 0.1%
 
241< 0.1%
 
251< 0.1%
 
ValueCountFrequency (%) 
20472271< 0.1%
 
20472261< 0.1%
 
20472231< 0.1%
 
20472221< 0.1%
 
20472201< 0.1%
 
20472141< 0.1%
 
20472131< 0.1%
 
20472111< 0.1%
 
20472081< 0.1%
 
20472011< 0.1%
 

FOLIO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct377431
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
1219430000
 
12
1488210176
 
11
0045299115
 
11
0036808582
 
11
0190301866
 
10
Other values (377426)
847047 
ValueCountFrequency (%) 
121943000012< 0.1%
 
148821017611< 0.1%
 
004529911511< 0.1%
 
003680858211< 0.1%
 
019030186610< 0.1%
 
118366000010< 0.1%
 
016123751210< 0.1%
 
003680844210< 0.1%
 
126415000010< 0.1%
 
014525243610< 0.1%
 
067476000010< 0.1%
 
027554762210< 0.1%
 
186317502610< 0.1%
 
054952341210< 0.1%
 
142577009010< 0.1%
 
005231715610< 0.1%
 
072369000010< 0.1%
 
120369000010< 0.1%
 
146209000010< 0.1%
 
057472360810< 0.1%
 
036675504610< 0.1%
 
157947000010< 0.1%
 
180030000010< 0.1%
 
149926010010< 0.1%
 
148172010010< 0.1%
 
Other values (377406)846847> 99.9%
 
2022-05-27T17:59:44.661000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique136797 ?
Unique (%)16.1%
2022-05-27T17:59:44.856628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8471020100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common8471020100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8471020100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0244647328.9%
 
18199849.7%
 
27709729.1%
 
56919368.2%
 
46905758.2%
 
76749458.0%
 
66457927.6%
 
86348837.5%
 
36238447.4%
 
94716165.6%
 

DOR_CODE
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
0100
655468 
0400
92814 
0106
73653 
0200
 
16019
0800
 
8243
Other values (3)
 
905
ValueCountFrequency (%) 
010065546877.4%
 
04009281411.0%
 
0106736538.7%
 
0200160191.9%
 
080082431.0%
 
04087580.1%
 
0801107< 0.1%
 
010240< 0.1%
 
2022-05-27T17:59:45.032938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:45.153705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:45.307005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3388408100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3388408100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3388408100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0246674872.8%
 
172926821.5%
 
4935722.8%
 
6736532.2%
 
2160590.5%
 
891080.3%
 

S_DATE
Date

Distinct8149
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1980-01-01 00:00:00
Maximum2022-01-28 00:00:00
2022-05-27T17:59:45.465170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:45.644260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VI
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
I
799492 
V
 
47610
ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 
2022-05-27T17:59:45.825053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:45.911109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:46.015389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I79949294.4%
 
V476105.6%
 

QU
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Q
847102 
ValueCountFrequency (%) 
Q847102100.0%
 
2022-05-27T17:59:46.149755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:46.254968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:46.320485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters1
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Q847102100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Q847102100.0%
 

REA_CD
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
01
543638 
02
255255 
2A
 
15502
2B
 
15440
00
 
14524
Other values (21)
 
2743
ValueCountFrequency (%) 
0154363864.2%
 
0225525530.1%
 
2A155021.8%
 
2B154401.8%
 
00145241.7%
 
3C17290.2%
 
3B243< 0.1%
 
38229< 0.1%
 
05205< 0.1%
 
3297< 0.1%
 
3A59< 0.1%
 
3D57< 0.1%
 
3733< 0.1%
 
1821< 0.1%
 
1219< 0.1%
 
3016< 0.1%
 
1115< 0.1%
 
206< 0.1%
 
193< 0.1%
 
352< 0.1%
 
342< 0.1%
 
142< 0.1%
 
982< 0.1%
 
211< 0.1%
 
131< 0.1%
 
2022-05-27T17:59:46.452113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2022-05-27T17:59:46.624261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
082816948.9%
 
154371532.1%
 
228632016.9%
 
B156830.9%
 
A155610.9%
 
324680.1%
 
C17290.1%
 
8252< 0.1%
 
5207< 0.1%
 
D57< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number166117498.1%
 
Uppercase Letter330301.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
082816949.9%
 
154371532.7%
 
228632017.2%
 
324680.1%
 
8252< 0.1%
 
5207< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B1568347.5%
 
A1556147.1%
 
C17295.2%
 
D570.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common166117498.1%
 
Latin330301.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
082816949.9%
 
154371532.7%
 
228632017.2%
 
324680.1%
 
8252< 0.1%
 
5207< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B1568347.5%
 
A1556147.1%
 
C17295.2%
 
D570.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
082816948.9%
 
154371532.1%
 
228632016.9%
 
B156830.9%
 
A155610.9%
 
324680.1%
 
C17290.1%
 
8252< 0.1%
 
5207< 0.1%
 
D57< 0.1%
 
733< 0.1%
 
95< 0.1%
 
45< 0.1%
 

S_AMT
Real number (ℝ≥0)

SKEWED

Distinct11127
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198455.6187
Minimum1100
Maximum26433000
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:46.774887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile32000
Q176600
median142000
Q3235000
95-th percentile465000
Maximum26433000
Range26431900
Interquartile range (IQR)158400

Descriptive statistics

Standard deviation393473.7381
Coefficient of variation (CV)1.98267875
Kurtosis686.9227945
Mean198455.6187
Median Absolute Deviation (MAD)73500
Skewness21.81267624
Sum1.681121515e+11
Variance1.548215825e+11
MonotocityNot monotonic
2022-05-27T17:59:46.925829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12500049880.6%
 
6500048440.6%
 
15000048310.6%
 
7500047950.6%
 
8500045040.5%
 
13500044090.5%
 
5000043940.5%
 
5500043030.5%
 
17500042910.5%
 
12000042870.5%
 
11000042620.5%
 
6000042610.5%
 
13000041880.5%
 
11500041600.5%
 
16500041360.5%
 
14000041190.5%
 
8000040680.5%
 
16000040660.5%
 
14500039920.5%
 
4500039850.5%
 
9000039670.5%
 
20000039660.5%
 
10000038670.5%
 
7000038130.5%
 
15500037800.4%
 
Other values (11102)74082687.5%
 
ValueCountFrequency (%) 
11001< 0.1%
 
11131< 0.1%
 
12007< 0.1%
 
13005< 0.1%
 
13751< 0.1%
 
14002< 0.1%
 
150013< 0.1%
 
15241< 0.1%
 
16002< 0.1%
 
16551< 0.1%
 
ValueCountFrequency (%) 
264330001< 0.1%
 
225195001< 0.1%
 
217350001< 0.1%
 
1688750040< 0.1%
 
1659360030< 0.1%
 
165036003< 0.1%
 
149407001< 0.1%
 
1358090053< 0.1%
 
128000001< 0.1%
 
117000001< 0.1%
 

SUB
Categorical

HIGH CARDINALITY

Distinct8814
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
ZZZ
 
34656
3U4
 
2756
509
 
2677
42J
 
2188
3TP
 
1815
Other values (8809)
803010 
ValueCountFrequency (%) 
ZZZ346564.1%
 
3U427560.3%
 
50926770.3%
 
42J21880.3%
 
3TP18150.2%
 
45M16650.2%
 
3LA14840.2%
 
45414590.2%
 
10414100.2%
 
3TR13470.2%
 
4PQ11440.1%
 
3T710490.1%
 
98M10400.1%
 
1TM10250.1%
 
82010160.1%
 
88J10060.1%
 
36C9950.1%
 
0BJ9880.1%
 
89N9770.1%
 
8639640.1%
 
9D79600.1%
 
0609570.1%
 
3D69480.1%
 
82P9350.1%
 
09K9340.1%
 
Other values (8789)78070792.2%
 
2022-05-27T17:59:47.182668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique274 ?
Unique (%)< 0.1%
2022-05-27T17:59:47.383901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01886527.4%
 
21730876.8%
 
31704486.7%
 
11575586.2%
 
51424725.6%
 
41416425.6%
 
Z1405315.5%
 
91141384.5%
 
8914813.6%
 
7909413.6%
 
6878723.5%
 
A624622.5%
 
B561072.2%
 
P534952.1%
 
T466591.8%
 
U460851.8%
 
X460611.8%
 
W443981.7%
 
V436581.7%
 
C421981.7%
 
Q416941.6%
 
J409911.6%
 
F404771.6%
 
E401991.6%
 
Y398271.6%
 
Other values (11)39817315.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number135829153.4%
 
Uppercase Letter118301546.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
018865213.9%
 
217308712.7%
 
317044812.5%
 
115755811.6%
 
514247210.5%
 
414164210.4%
 
91141388.4%
 
8914816.7%
 
7909416.7%
 
6878726.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Z14053111.9%
 
A624625.3%
 
B561074.7%
 
P534954.5%
 
T466593.9%
 
U460853.9%
 
X460613.9%
 
W443983.8%
 
V436583.7%
 
C421983.6%
 
Q416943.5%
 
J409913.5%
 
F404773.4%
 
E401993.4%
 
Y398273.4%
 
D393593.3%
 
I388513.3%
 
H381683.2%
 
R379423.2%
 
S374553.2%
 
L356643.0%
 
N345822.9%
 
M345702.9%
 
G343662.9%
 
O342592.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common135829153.4%
 
Latin118301546.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
018865213.9%
 
217308712.7%
 
317044812.5%
 
115755811.6%
 
514247210.5%
 
414164210.4%
 
91141388.4%
 
8914816.7%
 
7909416.7%
 
6878726.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Z14053111.9%
 
A624625.3%
 
B561074.7%
 
P534954.5%
 
T466593.9%
 
U460853.9%
 
X460613.9%
 
W443983.8%
 
V436583.7%
 
C421983.6%
 
Q416943.5%
 
J409913.5%
 
F404773.4%
 
E401993.4%
 
Y398273.4%
 
D393593.3%
 
I388513.3%
 
H381683.2%
 
R379423.2%
 
S374553.2%
 
L356643.0%
 
N345822.9%
 
M345702.9%
 
G343662.9%
 
O342592.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01886527.4%
 
21730876.8%
 
31704486.7%
 
11575586.2%
 
51424725.6%
 
41416425.6%
 
Z1405315.5%
 
91141384.5%
 
8914813.6%
 
7909413.6%
 
6878723.5%
 
A624622.5%
 
B561072.2%
 
P534952.1%
 
T466591.8%
 
U460851.8%
 
X460611.8%
 
W443981.7%
 
V436581.7%
 
C421981.7%
 
Q416941.6%
 
J409911.6%
 
F404771.6%
 
E401991.6%
 
Y398271.6%
 
Other values (11)39817315.7%
 

S_TYPE
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
WD
834201 
TR
 
6967
AG
 
2002
AD
 
1109
FD
 
924
Other values (14)
 
1899
ValueCountFrequency (%) 
WD83420198.5%
 
TR69670.8%
 
AG20020.2%
 
AD11090.1%
 
FD9240.1%
 
QC7080.1%
 
CT4480.1%
 
00225< 0.1%
 
DD119< 0.1%
 
PR103< 0.1%
 
GD98< 0.1%
 
AS89< 0.1%
 
CD38< 0.1%
 
TD31< 0.1%
 
ED18< 0.1%
 
MD18< 0.1%
 
WQ2< 0.1%
 
WS1< 0.1%
 
SD1< 0.1%
 
2022-05-27T17:59:47.521961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2022-05-27T17:59:47.701377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.2%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
0450< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1693754> 99.9%
 
Decimal Number450< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.3%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0450100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1693754> 99.9%
 
Common450< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.3%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0450100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
D83667649.4%
 
W83420449.2%
 
T74460.4%
 
R70700.4%
 
A32000.2%
 
G21000.1%
 
C11940.1%
 
F9240.1%
 
Q710< 0.1%
 
0450< 0.1%
 
P103< 0.1%
 
S91< 0.1%
 
E18< 0.1%
 
M18< 0.1%
 
Distinct6235
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Minimum1901-12-01 00:00:00
Maximum2022-01-19 00:00:00
2022-05-27T17:59:47.844289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:47.999010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SITE_ADDR
Categorical

HIGH CARDINALITY
UNIFORM

Distinct376952
Distinct (%)44.5%
Missing89
Missing (%)< 0.1%
Memory size6.5 MiB
611 DESTINY DR
 
233
4201 BAYSHORE BLVD
 
132
2001 E 2ND AVE
 
104
3507 BAYSHORE BLVD
 
76
0
 
36
Other values (376947)
846432 
ValueCountFrequency (%) 
611 DESTINY DR233< 0.1%
 
4201 BAYSHORE BLVD132< 0.1%
 
2001 E 2ND AVE104< 0.1%
 
3507 BAYSHORE BLVD76< 0.1%
 
036< 0.1%
 
3119 W DELEON ST27< 0.1%
 
1002 CHANNELSIDE DR22< 0.1%
 
1022 BELLASOL WAY12< 0.1%
 
902 S ROME AVE12< 0.1%
 
5026 W DICKENS AVE12< 0.1%
 
8523 J R MANOR DR11< 0.1%
 
12415 MONDRAGON DR11< 0.1%
 
7130 WATERSIDE DR11< 0.1%
 
16012 MARSHFIELD DR10< 0.1%
 
8601 N 39TH ST10< 0.1%
 
3109 W HAWTHORNE RD10< 0.1%
 
13404 PINE LAKE WAY10< 0.1%
 
1604 E NOME ST10< 0.1%
 
1026 BELLASOL WAY10< 0.1%
 
1705 E CHELSEA ST10< 0.1%
 
4911 E TEMPLE HEIGHTS RD A D10< 0.1%
 
18416 ORIOLE ST10< 0.1%
 
301 KNOTTWOOD CT10< 0.1%
 
5307 ABINGER CT10< 0.1%
 
136 BUTLER RD10< 0.1%
 
Other values (376927)84619499.9%
 
(Missing)89< 0.1%
 
2022-05-27T17:59:49.345770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique136558 ?
Unique (%)16.1%
2022-05-27T17:59:50.251158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length18
Mean length18.40164703
Min length1

Overview of Unicode Properties

Unique unicode characters57
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
230848314.8%
 
R10393636.7%
 
E9799426.3%
 
A8702545.6%
 
18550995.5%
 
D6637674.3%
 
L6562184.2%
 
N6105843.9%
 
05716953.7%
 
O5696583.7%
 
S5478113.5%
 
T5392203.5%
 
I4885973.1%
 
24723463.0%
 
C3616832.3%
 
33500422.2%
 
43212652.1%
 
W2806731.8%
 
52732821.8%
 
H2570461.6%
 
62478751.6%
 
V2441311.6%
 
82260671.5%
 
B2248701.4%
 
72221581.4%
 
Other values (32)14059439.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter953515261.2%
 
Decimal Number374228324.0%
 
Space Separator230848314.8%
 
Dash Punctuation938< 0.1%
 
Other Punctuation837< 0.1%
 
Lowercase Letter377< 0.1%
 
Modifier Symbol1< 0.1%
 
Open Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
185509922.8%
 
057169515.3%
 
247234612.6%
 
33500429.4%
 
43212658.6%
 
52732827.3%
 
62478756.6%
 
82260676.0%
 
72221585.9%
 
92024545.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2308483100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R103936310.9%
 
E97994210.3%
 
A8702549.1%
 
D6637677.0%
 
L6562186.9%
 
N6105846.4%
 
O5696586.0%
 
S5478115.7%
 
T5392205.7%
 
I4885975.1%
 
C3616833.8%
 
W2806732.9%
 
H2570462.7%
 
V2441312.6%
 
B2248702.4%
 
P2030872.1%
 
G1943222.0%
 
M1797811.9%
 
Y1783001.9%
 
K1679741.8%
 
U1448391.5%
 
F924761.0%
 
J135660.1%
 
X110030.1%
 
Z97410.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n19150.7%
 
a10026.5%
 
s174.5%
 
r143.7%
 
i123.2%
 
d112.9%
 
e112.9%
 
o102.7%
 
t82.1%
 
u10.3%
 
h10.3%
 
g10.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-938100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/53563.9%
 
#23227.7%
 
&586.9%
 
.111.3%
 
,10.1%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin953552961.2%
 
Common605254338.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
230848338.1%
 
185509914.1%
 
05716959.4%
 
24723467.8%
 
33500425.8%
 
43212655.3%
 
52732824.5%
 
62478754.1%
 
82260673.7%
 
72221583.7%
 
92024543.3%
 
-938< 0.1%
 
/535< 0.1%
 
#232< 0.1%
 
&58< 0.1%
 
.11< 0.1%
 
,1< 0.1%
 
`1< 0.1%
 
[1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R103936310.9%
 
E97994210.3%
 
A8702549.1%
 
D6637677.0%
 
L6562186.9%
 
N6105846.4%
 
O5696586.0%
 
S5478115.7%
 
T5392205.7%
 
I4885975.1%
 
C3616833.8%
 
W2806732.9%
 
H2570462.7%
 
V2441312.6%
 
B2248702.4%
 
P2030872.1%
 
G1943222.0%
 
M1797811.9%
 
Y1783001.9%
 
K1679741.8%
 
U1448391.5%
 
F924761.0%
 
J135660.1%
 
X110030.1%
 
Z97410.1%
 
Other values (13)66230.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15588072100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
230848314.8%
 
R10393636.7%
 
E9799426.3%
 
A8702545.6%
 
18550995.5%
 
D6637674.3%
 
L6562184.2%
 
N6105843.9%
 
05716953.7%
 
O5696583.7%
 
S5478113.5%
 
T5392203.5%
 
I4885973.1%
 
24723463.0%
 
C3616832.3%
 
33500422.2%
 
43212652.1%
 
W2806731.8%
 
52732821.8%
 
H2570461.6%
 
62478751.6%
 
V2441311.6%
 
82260671.5%
 
B2248701.4%
 
72221581.4%
 
Other values (32)14059439.0%
 

SITE_CITY
Categorical

HIGH CARDINALITY

Distinct66
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size6.5 MiB
TAMPA
441024 
RIVERVIEW
75729 
BRANDON
52580 
VALRICO
49710 
SUN CITY CENTER
 
33048
Other values (61)
195004 
ValueCountFrequency (%) 
TAMPA44102452.1%
 
RIVERVIEW757298.9%
 
BRANDON525806.2%
 
VALRICO497105.9%
 
SUN CITY CENTER330483.9%
 
PLANT CITY328383.9%
 
LUTZ299603.5%
 
APOLLO BEACH215072.5%
 
RUSKIN194182.3%
 
LITHIA172692.0%
 
TEMPLE TERRACE169952.0%
 
SEFFNER158261.9%
 
ODESSA122221.4%
 
WIMAUMA107901.3%
 
GIBSONTON80190.9%
 
DOVER56340.7%
 
THONOTOSASSA35000.4%
 
Tampa4430.1%
 
Unincorporated249< 0.1%
 
LAKELAND138< 0.1%
 
Plant City51< 0.1%
 
Temple Terrace37< 0.1%
 
ZEPHYRHILLS30< 0.1%
 
MULBERRY9< 0.1%
 
WIMAUAM6< 0.1%
 
Other values (41)63< 0.1%
 
(Missing)7< 0.1%
 
2022-05-27T17:59:50.408169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique26 ?
Unique (%)< 0.1%
2022-05-27T17:59:50.587803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length5
Mean length6.69774006
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A113560620.0%
 
T66957811.8%
 
P5124639.0%
 
M4796608.5%
 
R3617616.4%
 
E3567676.3%
 
I3399146.0%
 
N2590424.6%
 
V2068303.6%
 
L1901543.4%
 
O1897323.3%
 
C1872223.3%
 
1375562.4%
 
S1113022.0%
 
U935051.6%
 
W865481.5%
 
B821211.4%
 
D705801.2%
 
Y659351.2%
 
H423500.7%
 
F316540.6%
 
Z299900.5%
 
K195570.3%
 
G80280.1%
 
a1230< 0.1%
 
Other values (21)45840.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter553030497.5%
 
Space Separator1375562.4%
 
Lowercase Letter57940.1%
 
Decimal Number13< 0.1%
 
Modifier Symbol2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A113560620.5%
 
T66957812.1%
 
P5124639.3%
 
M4796608.7%
 
R3617616.5%
 
E3567676.5%
 
I3399146.1%
 
N2590424.7%
 
V2068303.7%
 
L1901543.4%
 
O1897323.4%
 
C1872223.4%
 
S1113022.0%
 
U935051.7%
 
W865481.6%
 
B821211.5%
 
D705801.3%
 
Y659351.2%
 
H423500.8%
 
F316540.6%
 
Z299900.5%
 
K195570.4%
 
G80280.1%
 
Q5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a123021.2%
 
p72912.6%
 
r5729.9%
 
n5639.7%
 
o4988.6%
 
m4808.3%
 
e3976.9%
 
t3516.1%
 
i3005.2%
 
c2864.9%
 
d2494.3%
 
l881.5%
 
y510.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
137556100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3430.8%
 
8215.4%
 
2215.4%
 
0215.4%
 
517.7%
 
617.7%
 
917.7%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin553609897.6%
 
Common1375712.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A113560620.5%
 
T66957812.1%
 
P5124639.3%
 
M4796608.7%
 
R3617616.5%
 
E3567676.4%
 
I3399146.1%
 
N2590424.7%
 
V2068303.7%
 
L1901543.4%
 
O1897323.4%
 
C1872223.4%
 
S1113022.0%
 
U935051.7%
 
W865481.6%
 
B821211.5%
 
D705801.3%
 
Y659351.2%
 
H423500.8%
 
F316540.6%
 
Z299900.5%
 
K195570.4%
 
G80280.1%
 
a1230< 0.1%
 
p729< 0.1%
 
Other values (12)38400.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
137556> 99.9%
 
34< 0.1%
 
`2< 0.1%
 
82< 0.1%
 
22< 0.1%
 
02< 0.1%
 
51< 0.1%
 
61< 0.1%
 
91< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5673669100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A113560620.0%
 
T66957811.8%
 
P5124639.0%
 
M4796608.5%
 
R3617616.4%
 
E3567676.3%
 
I3399146.0%
 
N2590424.6%
 
V2068303.6%
 
L1901543.4%
 
O1897323.3%
 
C1872223.3%
 
1375562.4%
 
S1113022.0%
 
U935051.6%
 
W865481.5%
 
B821211.4%
 
D705801.2%
 
Y659351.2%
 
H423500.7%
 
F316540.6%
 
Z299900.5%
 
K195570.3%
 
G80280.1%
 
a1230< 0.1%
 
Other values (21)45840.1%
 

SITE_ZIP
Categorical

HIGH CARDINALITY

Distinct2220
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
33647
 
44244
33573
 
36526
33624
 
35826
33511
 
33575
33578
 
31755
Other values (2215)
665176 
ValueCountFrequency (%) 
33647442445.2%
 
33573365264.3%
 
33624358264.2%
 
33511335754.0%
 
33578317553.7%
 
33615293763.5%
 
33611258443.1%
 
33596253673.0%
 
33604247662.9%
 
33579246682.9%
 
33626244092.9%
 
33594237712.8%
 
33617235742.8%
 
33629226802.7%
 
33572211552.5%
 
33618199952.4%
 
33625196482.3%
 
33614192842.3%
 
33612191292.3%
 
33510185362.2%
 
33569176752.1%
 
33547170582.0%
 
33584155721.8%
 
33570148091.7%
 
33610147111.7%
 
Other values (2195)24314928.7%
 
2022-05-27T17:59:50.728055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique724 ?
Unique (%)0.1%
2022-05-27T17:59:50.850356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length5.04094076
Min length5

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number425976299.8%
 
Dash Punctuation104250.2%
 
Connector Punctuation4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3180717542.4%
 
660580614.2%
 
549295211.6%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-10425100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4270191100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4270191100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3180717542.3%
 
660580614.2%
 
549295211.5%
 
13031857.1%
 
72471645.8%
 
42169405.1%
 
91751154.1%
 
21681303.9%
 
01448793.4%
 
8984162.3%
 
-104250.2%
 
_4< 0.1%
 

tBEDS
Real number (ℝ≥0)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.123815432
Minimum0
Maximum24
Zeros3792
Zeros (%)0.4%
Memory size6.5 MiB
2022-05-27T17:59:50.968723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9717916442
Coefficient of variation (CV)0.311091249
Kurtosis1.263989668
Mean3.123815432
Median Absolute Deviation (MAD)1
Skewness0.1646001513
Sum2646190.3
Variance0.9443789998
MonotocityNot monotonic
2022-05-27T17:59:51.113537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
335684842.1%
 
422943627.1%
 
217767421.0%
 
5454905.4%
 
1259243.1%
 
666570.8%
 
037920.4%
 
79170.1%
 
8173< 0.1%
 
948< 0.1%
 
1030< 0.1%
 
1124< 0.1%
 
2.523< 0.1%
 
3.523< 0.1%
 
0.311< 0.1%
 
139< 0.1%
 
1.59< 0.1%
 
128< 0.1%
 
5.53< 0.1%
 
151< 0.1%
 
241< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
037920.4%
 
0.311< 0.1%
 
1259243.1%
 
1.59< 0.1%
 
217767421.0%
 
2.523< 0.1%
 
335684842.1%
 
3.523< 0.1%
 
422943627.1%
 
5454905.4%
 
ValueCountFrequency (%) 
241< 0.1%
 
161< 0.1%
 
151< 0.1%
 
139< 0.1%
 
128< 0.1%
 
1124< 0.1%
 
1030< 0.1%
 
948< 0.1%
 
8173< 0.1%
 
79170.1%
 

tBATHS
Real number (ℝ≥0)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.222953552
Minimum0
Maximum17
Zeros3073
Zeros (%)0.4%
Memory size6.5 MiB
2022-05-27T17:59:51.292295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3.5
Maximum17
Range17
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.7817343878
Coefficient of variation (CV)0.3516647422
Kurtosis5.850254505
Mean2.222953552
Median Absolute Deviation (MAD)0
Skewness1.25144131
Sum1883068.4
Variance0.6111086531
MonotocityNot monotonic
2022-05-27T17:59:51.455137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
242434350.1%
 
2.512810615.1%
 
310831012.8%
 
19617911.4%
 
3.5284513.4%
 
1.5233612.8%
 
4192772.3%
 
4.577270.9%
 
536690.4%
 
030730.4%
 
5.522780.3%
 
68660.1%
 
6.57250.1%
 
7285< 0.1%
 
7.5197< 0.1%
 
878< 0.1%
 
8.564< 0.1%
 
931< 0.1%
 
1117< 0.1%
 
10.516< 0.1%
 
9.512< 0.1%
 
12.57< 0.1%
 
106< 0.1%
 
0.56< 0.1%
 
14.54< 0.1%
 
Other values (5)14< 0.1%
 
ValueCountFrequency (%) 
030730.4%
 
0.56< 0.1%
 
19617911.4%
 
1.14< 0.1%
 
1.5233612.8%
 
242434350.1%
 
2.512810615.1%
 
310831012.8%
 
3.5284513.4%
 
4192772.3%
 
ValueCountFrequency (%) 
171< 0.1%
 
14.54< 0.1%
 
141< 0.1%
 
12.57< 0.1%
 
124< 0.1%
 
11.54< 0.1%
 
1117< 0.1%
 
10.516< 0.1%
 
106< 0.1%
 
9.512< 0.1%
 

tSTORIES
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.294692375
Minimum0
Maximum11
Zeros1896
Zeros (%)0.2%
Memory size6.5 MiB
2022-05-27T17:59:51.590954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5010750046
Coefficient of variation (CV)0.3870224421
Kurtosis3.0621503
Mean1.294692375
Median Absolute Deviation (MAD)0
Skewness1.482789943
Sum1096736.5
Variance0.2510761602
MonotocityNot monotonic
2022-05-27T17:59:51.696642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
160628871.6%
 
222232126.2%
 
3105391.2%
 
1.538910.5%
 
018960.2%
 
415820.2%
 
2.5334< 0.1%
 
3.585< 0.1%
 
582< 0.1%
 
4.533< 0.1%
 
633< 0.1%
 
5.56< 0.1%
 
75< 0.1%
 
113< 0.1%
 
82< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
018960.2%
 
160628871.6%
 
1.538910.5%
 
222232126.2%
 
2.5334< 0.1%
 
3105391.2%
 
3.585< 0.1%
 
415820.2%
 
4.533< 0.1%
 
582< 0.1%
 
ValueCountFrequency (%) 
113< 0.1%
 
101< 0.1%
 
91< 0.1%
 
82< 0.1%
 
75< 0.1%
 
633< 0.1%
 
5.56< 0.1%
 
582< 0.1%
 
4.533< 0.1%
 
415820.2%
 

tUNITS
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.024708359
Minimum0
Maximum9
Zeros3055
Zeros (%)0.4%
Memory size6.5 MiB
2022-05-27T17:59:51.806960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.245855821
Coefficient of variation (CV)0.2399276037
Kurtosis233.9756271
Mean1.024708359
Median Absolute Deviation (MAD)0
Skewness12.08569953
Sum868032.5
Variance0.06044508471
MonotocityNot monotonic
2022-05-27T17:59:51.906719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
182668897.6%
 
2140741.7%
 
030550.4%
 
417810.2%
 
310600.1%
 
5139< 0.1%
 
8118< 0.1%
 
6114< 0.1%
 
741< 0.1%
 
931< 0.1%
 
3.51< 0.1%
 
ValueCountFrequency (%) 
030550.4%
 
182668897.6%
 
2140741.7%
 
310600.1%
 
3.51< 0.1%
 
417810.2%
 
5139< 0.1%
 
6114< 0.1%
 
741< 0.1%
 
8118< 0.1%
 
ValueCountFrequency (%) 
931< 0.1%
 
8118< 0.1%
 
741< 0.1%
 
6114< 0.1%
 
5139< 0.1%
 
417810.2%
 
3.51< 0.1%
 
310600.1%
 
2140741.7%
 
182668897.6%
 

tBLDGS
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8140684357
Minimum0
Maximum9
Zeros168348
Zeros (%)19.9%
Memory size6.5 MiB
2022-05-27T17:59:52.019132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4235400688
Coefficient of variation (CV)0.5202757535
Kurtosis2.087145125
Mean0.8140684357
Median Absolute Deviation (MAD)0
Skewness-0.909700508
Sum689599
Variance0.1793861899
MonotocityNot monotonic
2022-05-27T17:59:52.122975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
166865378.9%
 
016834819.9%
 
295431.1%
 
34290.1%
 
496< 0.1%
 
519< 0.1%
 
67< 0.1%
 
75< 0.1%
 
91< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
016834819.9%
 
166865378.9%
 
295431.1%
 
34290.1%
 
496< 0.1%
 
519< 0.1%
 
67< 0.1%
 
75< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
81< 0.1%
 
75< 0.1%
 
67< 0.1%
 
519< 0.1%
 
496< 0.1%
 
34290.1%
 
295431.1%
 
166865378.9%
 
016834819.9%
 

JUST
Real number (ℝ≥0)

HIGH CORRELATION

Distinct233533
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294145.4092
Minimum3650
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:52.421525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3650
5-th percentile88810
Q1179629
median246876.5
Q3340266.75
95-th percentile625102
Maximum16539559
Range16535909
Interquartile range (IQR)160637.75

Descriptive statistics

Standard deviation240877.7905
Coefficient of variation (CV)0.8189071901
Kurtosis249.1760728
Mean294145.4092
Median Absolute Deviation (MAD)77513.5
Skewness8.980293031
Sum2.491711644e+11
Variance5.802210995e+10
MonotocityNot monotonic
2022-05-27T17:59:52.618851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
74374398< 0.1%
 
71960373< 0.1%
 
153411369< 0.1%
 
56227362< 0.1%
 
89109361< 0.1%
 
65626347< 0.1%
 
73493336< 0.1%
 
102194317< 0.1%
 
73014302< 0.1%
 
161358294< 0.1%
 
66309287< 0.1%
 
68001285< 0.1%
 
103539278< 0.1%
 
55846277< 0.1%
 
118750274< 0.1%
 
60777274< 0.1%
 
146415234< 0.1%
 
77703230< 0.1%
 
45562230< 0.1%
 
82949228< 0.1%
 
30226228< 0.1%
 
80766225< 0.1%
 
47602222< 0.1%
 
47821219< 0.1%
 
111164218< 0.1%
 
Other values (233508)83993499.2%
 
ValueCountFrequency (%) 
365016< 0.1%
 
385022< 0.1%
 
400817< 0.1%
 
41848< 0.1%
 
42222< 0.1%
 
42663< 0.1%
 
443958< 0.1%
 
45811< 0.1%
 
47554< 0.1%
 
480054< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
122213255< 0.1%
 
109645312< 0.1%
 
95577754< 0.1%
 
93023244< 0.1%
 
87427943< 0.1%
 
82508801< 0.1%
 
78764261< 0.1%
 
76938662< 0.1%
 
75440332< 0.1%
 

LAND
Real number (ℝ≥0)

Distinct93658
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78944.39593
Minimum75
Maximum8673113
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:52.900581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile100
Q140859
median64260
Q391072
95-th percentile209664
Maximum8673113
Range8673038
Interquartile range (IQR)50213

Descriptive statistics

Standard deviation99046.77913
Coefficient of variation (CV)1.254639775
Kurtosis537.8256382
Mean78944.39593
Median Absolute Deviation (MAD)24870
Skewness13.5328729
Sum6.687395568e+10
Variance9810264456
MonotocityNot monotonic
2022-05-27T17:59:53.054239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1009343511.0%
 
8000018780.2%
 
21000017400.2%
 
3939015130.2%
 
5049013970.2%
 
6300013540.2%
 
20000013150.2%
 
5940012920.2%
 
3250012080.1%
 
6732011950.1%
 
4158010840.1%
 
12480010760.1%
 
5865010160.1%
 
468009550.1%
 
550008910.1%
 
693008890.1%
 
699938650.1%
 
550808550.1%
 
583288350.1%
 
328258160.1%
 
334758160.1%
 
720007890.1%
 
727207780.1%
 
499957710.1%
 
714007560.1%
 
Other values (93633)72758385.9%
 
ValueCountFrequency (%) 
7537< 0.1%
 
1009343511.0%
 
4021< 0.1%
 
6527< 0.1%
 
6543< 0.1%
 
9992< 0.1%
 
11951< 0.1%
 
13174< 0.1%
 
13291< 0.1%
 
13422< 0.1%
 
ValueCountFrequency (%) 
86731132< 0.1%
 
65022035< 0.1%
 
64621574< 0.1%
 
62541062< 0.1%
 
49655034< 0.1%
 
47736002< 0.1%
 
42436391< 0.1%
 
40202303< 0.1%
 
37444905< 0.1%
 
36732381< 0.1%
 

BLDG
Real number (ℝ≥0)

HIGH CORRELATION

Distinct196456
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206857.749
Minimum0
Maximum9840672
Zeros3111
Zeros (%)0.4%
Memory size6.5 MiB
2022-05-27T17:59:53.260479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68236
Q1130541
median175362
Q3238370.25
95-th percentile432959
Maximum9840672
Range9840672
Interquartile range (IQR)107829.25

Descriptive statistics

Standard deviation158482.9992
Coefficient of variation (CV)0.7661448504
Kurtosis173.734571
Mean206857.749
Median Absolute Deviation (MAD)51792.5
Skewness7.649765441
Sum1.752296129e+11
Variance2.511686102e+10
MonotocityNot monotonic
2022-05-27T17:59:53.458903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031110.4%
 
74274398< 0.1%
 
71860375< 0.1%
 
153311369< 0.1%
 
56127362< 0.1%
 
89009356< 0.1%
 
63987347< 0.1%
 
73393336< 0.1%
 
102094317< 0.1%
 
161258300< 0.1%
 
72914299< 0.1%
 
66209287< 0.1%
 
103439285< 0.1%
 
67901285< 0.1%
 
118650279< 0.1%
 
54207277< 0.1%
 
60677274< 0.1%
 
131773236< 0.1%
 
77603234< 0.1%
 
45462230< 0.1%
 
30126229< 0.1%
 
80387229< 0.1%
 
79490225< 0.1%
 
46914222< 0.1%
 
47721221< 0.1%
 
Other values (196431)83701998.8%
 
ValueCountFrequency (%) 
031110.4%
 
5162< 0.1%
 
5233< 0.1%
 
6333< 0.1%
 
7172< 0.1%
 
8091< 0.1%
 
8102< 0.1%
 
8711< 0.1%
 
8792< 0.1%
 
9222< 0.1%
 
ValueCountFrequency (%) 
98406724< 0.1%
 
70642304< 0.1%
 
66046162< 0.1%
 
58766471< 0.1%
 
56157695< 0.1%
 
52846203< 0.1%
 
50675221< 0.1%
 
49170342< 0.1%
 
49070071< 0.1%
 
47542751< 0.1%
 

EXF
Real number (ℝ≥0)

ZEROS

Distinct34660
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8274.239497
Minimum0
Maximum409781
Zeros316973
Zeros (%)37.4%
Memory size6.5 MiB
2022-05-27T17:59:53.633010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1361
Q312173
95-th percentile35171
Maximum409781
Range409781
Interquartile range (IQR)12173

Descriptive statistics

Standard deviation13634.71872
Coefficient of variation (CV)1.647851592
Kurtosis20.07374653
Mean8274.239497
Median Absolute Deviation (MAD)1361
Skewness2.871606987
Sum7009124826
Variance185905554.7
MonotocityNot monotonic
2022-05-27T17:59:53.787647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
031697337.4%
 
2502107091.3%
 
262736720.4%
 
950032830.4%
 
275229950.4%
 
2531226000.3%
 
300225290.3%
 
1335024160.3%
 
2467123480.3%
 
200219900.2%
 
1585218050.2%
 
2371016710.2%
 
2252716210.2%
 
287715330.2%
 
1602014940.2%
 
312814760.2%
 
760014340.2%
 
330314150.2%
 
2109314000.2%
 
1975813960.2%
 
2055913790.2%
 
1902213720.2%
 
1200213240.2%
 
2274813000.2%
 
2395912840.2%
 
Other values (34635)47568356.2%
 
ValueCountFrequency (%) 
031697337.4%
 
51< 0.1%
 
62< 0.1%
 
92< 0.1%
 
152< 0.1%
 
162< 0.1%
 
186< 0.1%
 
416< 0.1%
 
422< 0.1%
 
573< 0.1%
 
ValueCountFrequency (%) 
4097811< 0.1%
 
3774421< 0.1%
 
3668303< 0.1%
 
3549261< 0.1%
 
3278141< 0.1%
 
3145271< 0.1%
 
2853851< 0.1%
 
2850011< 0.1%
 
2847451< 0.1%
 
2845223< 0.1%
 

ACT
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size49.2 MiB

EFF
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size49.2 MiB

HEAT_AR
Real number (ℝ≥0)

Distinct6163
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1860.356963
Minimum0
Maximum28893
Zeros1813
Zeros (%)0.2%
Memory size6.5 MiB
2022-05-27T17:59:53.906755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile890
Q11283
median1674
Q32237
95-th percentile3424
Maximum28893
Range28893
Interquartile range (IQR)954

Descriptive statistics

Standard deviation853.5299338
Coefficient of variation (CV)0.4587990105
Kurtosis15.15899919
Mean1860.356963
Median Absolute Deviation (MAD)443
Skewness2.164527077
Sum1575912104
Variance728513.348
MonotocityNot monotonic
2022-05-27T17:59:54.013353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120038380.5%
 
96035050.4%
 
115226680.3%
 
129622470.3%
 
91222440.3%
 
124822390.3%
 
151621550.3%
 
118420260.2%
 
126020230.2%
 
80019520.2%
 
144019520.2%
 
140418640.2%
 
127218490.2%
 
134418470.2%
 
108018170.2%
 
018130.2%
 
98417500.2%
 
117616730.2%
 
124416480.2%
 
116415790.2%
 
110415530.2%
 
128015390.2%
 
140015310.2%
 
151215190.2%
 
140815040.2%
 
Other values (6138)79676794.1%
 
ValueCountFrequency (%) 
018130.2%
 
1401< 0.1%
 
1602< 0.1%
 
1922< 0.1%
 
2001< 0.1%
 
2162< 0.1%
 
2321< 0.1%
 
2401< 0.1%
 
2722< 0.1%
 
2803< 0.1%
 
ValueCountFrequency (%) 
288931< 0.1%
 
283631< 0.1%
 
217964< 0.1%
 
216381< 0.1%
 
189123< 0.1%
 
187032< 0.1%
 
185684< 0.1%
 
183993< 0.1%
 
177404< 0.1%
 
174001< 0.1%
 

ASD_VAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct224721
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211092.1663
Minimum2725
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:54.253917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2725
5-th percentile54005
Q1112457
median172631
Q3251333
95-th percentile478374.3
Maximum16539559
Range16536834
Interquartile range (IQR)138876

Descriptive statistics

Standard deviation193869.4509
Coefficient of variation (CV)0.918411395
Kurtosis404.3873584
Mean211092.1663
Median Absolute Deviation (MAD)67164
Skewness10.43269959
Sum1.788165963e+11
Variance3.7585364e+10
MonotocityNot monotonic
2022-05-27T17:59:54.397750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
92485244< 0.1%
 
74374210< 0.1%
 
56227194< 0.1%
 
117809193< 0.1%
 
30226188< 0.1%
 
71960180< 0.1%
 
80766164< 0.1%
 
100161154< 0.1%
 
157653142< 0.1%
 
30000140< 0.1%
 
59940139< 0.1%
 
146415136< 0.1%
 
119904134< 0.1%
 
38038127< 0.1%
 
66447127< 0.1%
 
95882126< 0.1%
 
45562125< 0.1%
 
111405125< 0.1%
 
66309124< 0.1%
 
61606120< 0.1%
 
45113118< 0.1%
 
92706118< 0.1%
 
65626107< 0.1%
 
47602105< 0.1%
 
126537104< 0.1%
 
Other values (224696)84345899.6%
 
ValueCountFrequency (%) 
27255< 0.1%
 
33533< 0.1%
 
35133< 0.1%
 
35417< 0.1%
 
365016< 0.1%
 
37523< 0.1%
 
385022< 0.1%
 
38932< 0.1%
 
39372< 0.1%
 
400817< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
105903845< 0.1%
 
87427943< 0.1%
 
81439044< 0.1%
 
74543331< 0.1%
 
73608962< 0.1%
 
72319771< 0.1%
 
65270161< 0.1%
 
63094414< 0.1%
 
61745021< 0.1%
 

TAX_VAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct218493
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180119.1973
Minimum31
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:54.636034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile25000
Q178423
median140666
Q3222943
95-th percentile447840
Maximum16539559
Range16539528
Interquartile range (IQR)144520

Descriptive statistics

Standard deviation193875.4085
Coefficient of variation (CV)1.076372821
Kurtosis405.7613141
Mean180119.1973
Median Absolute Deviation (MAD)69536
Skewness10.42827929
Sum1.525793323e+11
Variance3.758767401e+10
MonotocityNot monotonic
2022-05-27T17:59:54.754467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25000306313.6%
 
2450025180.3%
 
200004390.1%
 
92485244< 0.1%
 
74374199< 0.1%
 
117809195< 0.1%
 
56227191< 0.1%
 
30226189< 0.1%
 
71960171< 0.1%
 
100161154< 0.1%
 
80766153< 0.1%
 
59940147< 0.1%
 
157653142< 0.1%
 
30000135< 0.1%
 
66309131< 0.1%
 
66447130< 0.1%
 
111405130< 0.1%
 
45562127< 0.1%
 
95882127< 0.1%
 
61606126< 0.1%
 
38038125< 0.1%
 
119904124< 0.1%
 
146415124< 0.1%
 
45113119< 0.1%
 
92706109< 0.1%
 
Other values (218468)81022295.6%
 
ValueCountFrequency (%) 
311< 0.1%
 
324< 0.1%
 
341< 0.1%
 
371< 0.1%
 
391< 0.1%
 
441< 0.1%
 
551< 0.1%
 
593< 0.1%
 
661< 0.1%
 
771< 0.1%
 
ValueCountFrequency (%) 
165395594< 0.1%
 
105403845< 0.1%
 
87427943< 0.1%
 
80939044< 0.1%
 
74543331< 0.1%
 
73108962< 0.1%
 
72319771< 0.1%
 
64770161< 0.1%
 
62594414< 0.1%
 
61745021< 0.1%
 

SD1
Categorical

HIGH CARDINALITY

Distinct169
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
000
635962 
702
 
16449
006
 
13048
154
 
11061
037
 
10568
Other values (164)
160014 
ValueCountFrequency (%) 
00063596275.1%
 
702164491.9%
 
006130481.5%
 
154110611.3%
 
037105681.2%
 
00574220.9%
 
04763340.7%
 
01258520.7%
 
04352960.6%
 
01146120.5%
 
04136090.4%
 
05331260.4%
 
03430780.4%
 
06330240.4%
 
YGR28690.3%
 
04427590.3%
 
06127120.3%
 
02126370.3%
 
09726250.3%
 
07724680.3%
 
11624430.3%
 
06224350.3%
 
05723810.3%
 
00221370.3%
 
00321140.2%
 
Other values (144)9008110.6%
 
2022-05-27T17:59:54.927602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:55.095091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0212026783.4%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 
Y28690.1%
 
G28690.1%
 
R28690.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number253269999.7%
 
Uppercase Letter86070.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0212026783.7%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y286933.3%
 
G286933.3%
 
R286933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common253269999.7%
 
Latin86070.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0212026783.7%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y286933.3%
 
G286933.3%
 
R286933.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0212026783.4%
 
1916943.6%
 
7576742.3%
 
4528442.1%
 
2438631.7%
 
5415681.6%
 
3409171.6%
 
6408731.6%
 
9258821.0%
 
8171170.7%
 
Y28690.1%
 
G28690.1%
 
R28690.1%
 

SD2
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
000
842332 
201
 
3578
YGR
 
795
928
 
209
929
 
150
Other values (3)
 
38
ValueCountFrequency (%) 
00084233299.4%
 
20135780.4%
 
YGR7950.1%
 
928209< 0.1%
 
929150< 0.1%
 
70233< 0.1%
 
0073< 0.1%
 
1402< 0.1%
 
2022-05-27T17:59:55.231252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:55.302014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:55.453706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0253061599.6%
 
239700.2%
 
135800.1%
 
Y795< 0.1%
 
G795< 0.1%
 
R795< 0.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number253892199.9%
 
Uppercase Letter23850.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0253061599.7%
 
239700.2%
 
135800.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y79533.3%
 
G79533.3%
 
R79533.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common253892199.9%
 
Latin23850.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0253061599.7%
 
239700.2%
 
135800.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y79533.3%
 
G79533.3%
 
R79533.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2541306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0253061599.6%
 
239700.2%
 
135800.1%
 
Y795< 0.1%
 
G795< 0.1%
 
R795< 0.1%
 
9509< 0.1%
 
8209< 0.1%
 
736< 0.1%
 
42< 0.1%
 

TIF
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
0
785718 
E
 
30826
9
 
14563
1
 
5445
C
 
2242
Other values (11)
 
8308
ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 
2022-05-27T17:59:55.638430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:55.811019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number81215495.9%
 
Uppercase Letter349484.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
078571896.7%
 
9145631.8%
 
154450.7%
 
618640.2%
 
213760.2%
 
312540.2%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
74< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E3082688.2%
 
C22426.4%
 
D14904.3%
 
A1910.5%
 
N1870.5%
 
B12< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common81215495.9%
 
Latin349484.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
078571896.7%
 
9145631.8%
 
154450.7%
 
618640.2%
 
213760.2%
 
312540.2%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
74< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E3082688.2%
 
C22426.4%
 
D14904.3%
 
A1910.5%
 
N1870.5%
 
B12< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
078571892.8%
 
E308263.6%
 
9145631.7%
 
154450.6%
 
C22420.3%
 
618640.2%
 
D14900.2%
 
213760.2%
 
312540.1%
 
49640.1%
 
56910.1%
 
8275< 0.1%
 
A191< 0.1%
 
N187< 0.1%
 
B12< 0.1%
 
74< 0.1%
 

BASE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size42.3 MiB

ACREAGE
Real number (ℝ≥0)

SKEWED

Distinct217700
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2779287273
Minimum2.03714e-05
Maximum102.646
Zeros0
Zeros (%)0.0%
Memory size6.5 MiB
2022-05-27T17:59:56.116253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.03714e-05
5-th percentile0.0100321
Q10.11460825
median0.1693355
Q30.250517
95-th percentile0.89392
Maximum102.646
Range102.6459796
Interquartile range (IQR)0.13590875

Descriptive statistics

Standard deviation0.6973655742
Coefficient of variation (CV)2.509152547
Kurtosis4767.892591
Mean0.2779287273
Median Absolute Deviation (MAD)0.0646365
Skewness41.55003936
Sum235433.9808
Variance0.4863187441
MonotocityNot monotonic
2022-05-27T17:59:56.270756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.16527e-05118921.4%
 
9.1827e-05110841.3%
 
5.1664e-0534670.4%
 
0.12626328980.3%
 
5.16414e-0528370.3%
 
0.13774119350.2%
 
0.12626216660.2%
 
0.1010114800.2%
 
0.12626411180.1%
 
0.1147849240.1%
 
0.1320028020.1%
 
0.01019297380.1%
 
0.1515157340.1%
 
0.137746870.1%
 
0.1388896490.1%
 
0.1010116420.1%
 
0.1262656150.1%
 
0.01019286060.1%
 
0.1377425970.1%
 
0.1652895930.1%
 
0.1262665520.1%
 
0.1262615500.1%
 
0.1101935390.1%
 
0.1147835060.1%
 
0.1010094980.1%
 
Other values (217675)79849394.3%
 
ValueCountFrequency (%) 
2.03714e-055< 0.1%
 
3.80899e-053< 0.1%
 
4.20554e-051< 0.1%
 
4.4168e-052< 0.1%
 
4.72805e-051< 0.1%
 
4.78615e-052< 0.1%
 
4.84166e-052< 0.1%
 
4.89977e-052< 0.1%
 
5.16177e-052< 0.1%
 
5.16219e-051< 0.1%
 
ValueCountFrequency (%) 
102.6468< 0.1%
 
52.23651< 0.1%
 
47.85982< 0.1%
 
41.41011< 0.1%
 
40.8991< 0.1%
 
40.30082< 0.1%
 
39.7321< 0.1%
 
39.6944< 0.1%
 
38.60562< 0.1%
 
37.2812< 0.1%
 

NBHC
Categorical

HIGH CARDINALITY

Distinct313
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
228003.0
 
20376
224005.0
 
15718
228004.0
 
11840
225001.0
 
11481
212003.0
 
10912
Other values (308)
776775 
ValueCountFrequency (%) 
228003.0203762.4%
 
224005.0157181.9%
 
228004.0118401.4%
 
225001.0114811.4%
 
212003.0109121.3%
 
212004.094811.1%
 
227001.092261.1%
 
226002.083911.0%
 
220003.083631.0%
 
212006.081961.0%
 
223008.073960.9%
 
202001.073370.9%
 
209012.072580.9%
 
222006.065270.8%
 
222002.064190.8%
 
212008.062370.7%
 
226001.062240.7%
 
227003.061550.7%
 
223001.061340.7%
 
223009.060780.7%
 
220004.059990.7%
 
216002.058840.7%
 
214006.058690.7%
 
226003.056810.7%
 
223007.056330.7%
 
Other values (288)63828775.3%
 
2022-05-27T17:59:56.489663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:56.659543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number592971487.5%
 
Other Punctuation84710212.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0273359646.1%
 
2141458123.9%
 
161495910.4%
 
32477214.2%
 
62209163.7%
 
51756723.0%
 
41684642.8%
 
71345082.3%
 
81254322.1%
 
9938651.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.847102100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6776816100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6776816100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0273359640.3%
 
2141458120.9%
 
.84710212.5%
 
16149599.1%
 
32477213.7%
 
62209163.3%
 
51756722.6%
 
41684642.5%
 
71345082.0%
 
81254321.9%
 
9938651.4%
 

MUNICIPALITY_CD
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
U
589960 
A
220419 
P
 
20141
T
 
16582
ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 
2022-05-27T17:59:56.796256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:56.881440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:56.995374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin847102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII847102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U58996069.6%
 
A22041926.0%
 
P201412.4%
 
T165822.0%
 

SECTION_CD
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.7 KiB
10
 
30219
06
 
29739
05
 
29452
07
 
29412
12
 
28773
Other values (31)
699507 
ValueCountFrequency (%) 
10302193.6%
 
06297393.5%
 
05294523.5%
 
07294123.5%
 
12287733.4%
 
17285273.4%
 
33280033.3%
 
04279793.3%
 
32263063.1%
 
11261053.1%
 
20256353.0%
 
08253223.0%
 
21243972.9%
 
14243112.9%
 
28240282.8%
 
23237392.8%
 
22231932.7%
 
18231702.7%
 
36227992.7%
 
26226882.7%
 
15226292.7%
 
13224862.7%
 
27223342.6%
 
25221952.6%
 
16218812.6%
 
Other values (11)21178025.0%
 
2022-05-27T17:59:57.197052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:57.365120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
133132619.6%
 
232757519.3%
 
030066917.7%
 
324437014.4%
 
6971075.7%
 
4931105.5%
 
5926005.5%
 
7802734.7%
 
8725204.3%
 
9546543.2%
 

TOWNSHIP_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
28
252681 
29
213671 
30
144807 
27
118262 
31
65403 
ValueCountFrequency (%) 
2825268129.8%
 
2921367125.2%
 
3014480717.1%
 
2711826214.0%
 
31654037.7%
 
32522786.2%
 
2022-05-27T17:59:57.500754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:57.580411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:57.691494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
263689237.6%
 
326248815.5%
 
825268114.9%
 
921367112.6%
 
01448078.5%
 
71182627.0%
 
1654033.9%
 

RANGE_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.6 KiB
18
246935 
20
210497 
19
200799 
17
99602 
21
68313 
ValueCountFrequency (%) 
1824693529.2%
 
2021049724.8%
 
1920079923.7%
 
179960211.8%
 
21683138.1%
 
22209562.5%
 
2022-05-27T17:59:57.776597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:57.877181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:57.979699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
161564936.3%
 
232072218.9%
 
824693514.6%
 
021049712.4%
 
920079911.9%
 
7996025.9%
 

BLOCK_NUM
Categorical

HIGH CARDINALITY

Distinct887
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
000000
207148 
000001
100720 
000002
76191 
000003
56489 
000004
44817 
Other values (882)
361737 
ValueCountFrequency (%) 
00000020714824.5%
 
00000110072011.9%
 
000002761919.0%
 
000003564896.7%
 
000004448175.3%
 
000005362974.3%
 
000006249462.9%
 
000007193902.3%
 
000008167192.0%
 
A00000159091.9%
 
000009146881.7%
 
000010128181.5%
 
B00000124241.5%
 
000011102551.2%
 
C0000094371.1%
 
00001288431.0%
 
00001479750.9%
 
00001379160.9%
 
00001568750.8%
 
00001666330.8%
 
D0000063840.8%
 
00001760090.7%
 
00001846270.5%
 
E0000046120.5%
 
00001944570.5%
 
Other values (862)12452314.7%
 
2022-05-27T17:59:58.119958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20 ?
Unique (%)< 0.1%
2022-05-27T17:59:58.222619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0427240184.1%
 
12092694.1%
 
21342352.6%
 
3980461.9%
 
4740701.5%
 
5616891.2%
 
6475390.9%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 
A187480.4%
 
B161070.3%
 
C113810.2%
 
D77890.2%
 
E57860.1%
 
F37070.1%
 
G31990.1%
 
H2488< 0.1%
 
I2069< 0.1%
 
K1545< 0.1%
 
J1442< 0.1%
 
L1145< 0.1%
 
T941< 0.1%
 
N822< 0.1%
 
M804< 0.1%
 
Other values (11)43050.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number500033498.4%
 
Uppercase Letter822781.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0427240185.4%
 
12092694.2%
 
21342352.7%
 
3980462.0%
 
4740701.5%
 
5616891.2%
 
6475391.0%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1874822.8%
 
B1610719.6%
 
C1138113.8%
 
D77899.5%
 
E57867.0%
 
F37074.5%
 
G31993.9%
 
H24883.0%
 
I20692.5%
 
K15451.9%
 
J14421.8%
 
L11451.4%
 
T9411.1%
 
N8221.0%
 
M8041.0%
 
P7700.9%
 
S6940.8%
 
Q5950.7%
 
O5930.7%
 
R5560.7%
 
U3030.4%
 
V2730.3%
 
W2620.3%
 
X1560.2%
 
Y560.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common500033498.4%
 
Latin822781.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
0427240185.4%
 
12092694.2%
 
21342352.7%
 
3980462.0%
 
4740701.5%
 
5616891.2%
 
6475391.0%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A1874822.8%
 
B1610719.6%
 
C1138113.8%
 
D77899.5%
 
E57867.0%
 
F37074.5%
 
G31993.9%
 
H24883.0%
 
I20692.5%
 
K15451.9%
 
J14421.8%
 
L11451.4%
 
T9411.1%
 
N8221.0%
 
M8041.0%
 
P7700.9%
 
S6940.8%
 
Q5950.7%
 
O5930.7%
 
R5560.7%
 
U3030.4%
 
V2730.3%
 
W2620.3%
 
X1560.2%
 
Y560.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5082612100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0427240184.1%
 
12092694.1%
 
21342352.6%
 
3980461.9%
 
4740701.5%
 
5616891.2%
 
6475390.9%
 
7393630.8%
 
8331090.7%
 
9306130.6%
 
A187480.4%
 
B161070.3%
 
C113810.2%
 
D77890.2%
 
E57860.1%
 
F37070.1%
 
G31990.1%
 
H2488< 0.1%
 
I2069< 0.1%
 
K1545< 0.1%
 
J1442< 0.1%
 
L1145< 0.1%
 
T941< 0.1%
 
N822< 0.1%
 
M804< 0.1%
 
Other values (11)43050.1%
 

LOT_NUM
Categorical

HIGH CARDINALITY

Distinct19649
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
00001.0
 
36125
00003.0
 
34674
00002.0
 
33764
00004.0
 
33354
00005.0
 
31269
Other values (19644)
677916 
ValueCountFrequency (%) 
00001.0361254.3%
 
00003.0346744.1%
 
00002.0337644.0%
 
00004.0333543.9%
 
00005.0312693.7%
 
00006.0294773.5%
 
00007.0265003.1%
 
00008.0253573.0%
 
00009.0227942.7%
 
00010.0215732.5%
 
00011.0209402.5%
 
00012.0190382.2%
 
00013.0187822.2%
 
00014.0174782.1%
 
00015.0168382.0%
 
00016.0158501.9%
 
00017.0152071.8%
 
00018.0140681.7%
 
00019.0134231.6%
 
00020.0124691.5%
 
00021.0117391.4%
 
00022.0111991.3%
 
00023.0104401.2%
 
00024.094661.1%
 
00025.086901.0%
 
Other values (19624)33658839.7%
 
2022-05-27T17:59:58.418588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3367 ?
Unique (%)0.4%
2022-05-27T17:59:58.568237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0355236559.9%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 
A45940.1%
 
B36190.1%
 
C2336< 0.1%
 
D1688< 0.1%
 
E1494< 0.1%
 
F867< 0.1%
 
G657< 0.1%
 
H563< 0.1%
 
W542< 0.1%
 
J410< 0.1%
 
L362< 0.1%
 
I341< 0.1%
 
K307< 0.1%
 
N286< 0.1%
 
Other values (10)1546< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number506300085.4%
 
Other Punctuation84710214.3%
 
Uppercase Letter196120.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0355236570.2%
 
13842557.6%
 
22461644.9%
 
31844053.6%
 
41495173.0%
 
51299042.6%
 
61169962.3%
 
71072542.1%
 
81008192.0%
 
9913211.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.847102100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A459423.4%
 
B361918.5%
 
C233611.9%
 
D16888.6%
 
E14947.6%
 
F8674.4%
 
G6573.3%
 
H5632.9%
 
W5422.8%
 
J4102.1%
 
L3621.8%
 
I3411.7%
 
K3071.6%
 
N2861.5%
 
M2821.4%
 
S2661.4%
 
T2251.1%
 
P2121.1%
 
V1700.9%
 
O1270.6%
 
Q940.5%
 
R690.4%
 
X650.3%
 
U360.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common591010299.7%
 
Latin196120.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0355236560.1%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A459423.4%
 
B361918.5%
 
C233611.9%
 
D16888.6%
 
E14947.6%
 
F8674.4%
 
G6573.3%
 
H5632.9%
 
W5422.8%
 
J4102.1%
 
L3621.8%
 
I3411.7%
 
K3071.6%
 
N2861.5%
 
M2821.4%
 
S2661.4%
 
T2251.1%
 
P2121.1%
 
V1700.9%
 
O1270.6%
 
Q940.5%
 
R690.4%
 
X650.3%
 
U360.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5929714100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0355236559.9%
 
.84710214.3%
 
13842556.5%
 
22461644.2%
 
31844053.1%
 
41495172.5%
 
51299042.2%
 
61169962.0%
 
71072541.8%
 
81008191.7%
 
9913211.5%
 
A45940.1%
 
B36190.1%
 
C2336< 0.1%
 
D1688< 0.1%
 
E1494< 0.1%
 
F867< 0.1%
 
G657< 0.1%
 
H563< 0.1%
 
W542< 0.1%
 
J410< 0.1%
 
L362< 0.1%
 
I341< 0.1%
 
K307< 0.1%
 
N286< 0.1%
 
Other values (10)1546< 0.1%
 

MARKET_AREA_CD
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
26
 
58031
12
 
55339
23
 
52030
20
 
46806
16
 
46728
Other values (24)
588168 
ValueCountFrequency (%) 
26580316.9%
 
12553396.5%
 
23520306.1%
 
20468065.5%
 
16467285.5%
 
03393934.7%
 
27383854.5%
 
28367854.3%
 
15367544.3%
 
21303753.6%
 
10297423.5%
 
24295683.5%
 
06289013.4%
 
09276193.3%
 
05274323.2%
 
22262103.1%
 
14245122.9%
 
25238002.8%
 
13232542.7%
 
02231092.7%
 
08228062.7%
 
17225972.7%
 
01220542.6%
 
04159821.9%
 
07147021.7%
 
Other values (4)441885.2%
 
2022-05-27T17:59:58.734949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:58.902748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
245427926.8%
 
134162020.2%
 
029854617.6%
 
61336607.9%
 
31146776.8%
 
5879865.2%
 
7756844.5%
 
8729934.3%
 
4700624.1%
 
9446972.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1694204100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
245427926.8%
 
134162020.2%
 
029854617.6%
 
61336607.9%
 
31146776.8%
 
5879865.2%
 
7756844.5%
 
8729934.3%
 
4700624.1%
 
9446972.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1694204100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
245427926.8%
 
134162020.2%
 
029854617.6%
 
61336607.9%
 
31146776.8%
 
5879865.2%
 
7756844.5%
 
8729934.3%
 
4700624.1%
 
9446972.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1694204100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
245427926.8%
 
134162020.2%
 
029854617.6%
 
61336607.9%
 
31146776.8%
 
5879865.2%
 
7756844.5%
 
8729934.3%
 
4700624.1%
 
9446972.6%
 

REGION
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
Northern
209184 
Eastern
163429 
Tampa
109823 
Northeast
100030 
South_Tampa
84556 
Other values (3)
180080 
ValueCountFrequency (%) 
Northern20918424.7%
 
Eastern16342919.3%
 
Tampa10982313.0%
 
Northeast10003011.8%
 
South_Tampa8455610.0%
 
Northwest710698.4%
 
East_Bay645957.6%
 
Southern444165.2%
 
2022-05-27T17:59:59.001806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-27T17:59:59.098944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:59.221456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length7.919571669
Min length5

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t90837813.5%
 
r79731211.9%
 
a78140711.6%
 
e5881288.8%
 
o5092557.6%
 
h5092557.6%
 
n4170296.2%
 
s3991235.9%
 
N3802835.7%
 
E2280243.4%
 
T1943792.9%
 
m1943792.9%
 
p1943792.9%
 
_1491512.2%
 
S1289721.9%
 
u1289721.9%
 
w710691.1%
 
B645951.0%
 
y645951.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter556328182.9%
 
Uppercase Letter99625314.9%
 
Connector Punctuation1491512.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N38028338.2%
 
E22802422.9%
 
T19437919.5%
 
S12897212.9%
 
B645956.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t90837816.3%
 
r79731214.3%
 
a78140714.0%
 
e58812810.6%
 
o5092559.2%
 
h5092559.2%
 
n4170297.5%
 
s3991237.2%
 
m1943793.5%
 
p1943793.5%
 
u1289722.3%
 
w710691.3%
 
y645951.2%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_149151100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin655953497.8%
 
Common1491512.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t90837813.8%
 
r79731212.2%
 
a78140711.9%
 
e5881289.0%
 
o5092557.8%
 
h5092557.8%
 
n4170296.4%
 
s3991236.1%
 
N3802835.8%
 
E2280243.5%
 
T1943793.0%
 
m1943793.0%
 
p1943793.0%
 
S1289722.0%
 
u1289722.0%
 
w710691.1%
 
B645951.0%
 
y645951.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
_149151100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6708685100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t90837813.5%
 
r79731211.9%
 
a78140711.6%
 
e5881288.8%
 
o5092557.6%
 
h5092557.6%
 
n4170296.2%
 
s3991235.9%
 
N3802835.7%
 
E2280243.4%
 
T1943792.9%
 
m1943792.9%
 
p1943792.9%
 
_1491512.2%
 
S1289721.9%
 
u1289721.9%
 
w710691.1%
 
B645951.0%
 
y645951.0%
 

AGE
Real number (ℝ)

ZEROS

Distinct176
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.28856855
Minimum-41
Maximum143
Zeros177637
Zeros (%)21.0%
Memory size6.5 MiB
2022-05-27T17:59:59.418666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-41
5-th percentile-1
Q10
median11
Q328
95-th percentile60
Maximum143
Range184
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.88143393
Coefficient of variation (CV)1.207817401
Kurtosis1.758317596
Mean17.28856855
Median Absolute Deviation (MAD)11
Skewness1.302518853
Sum14645181
Variance436.0342831
MonotocityNot monotonic
2022-05-27T17:59:59.605285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
017763721.0%
 
1290503.4%
 
4200272.4%
 
3192602.3%
 
5191082.3%
 
2183302.2%
 
6175032.1%
 
7170822.0%
 
-1170602.0%
 
9167902.0%
 
8167342.0%
 
12163721.9%
 
10163681.9%
 
11162761.9%
 
14161211.9%
 
13158431.9%
 
15157931.9%
 
16147051.7%
 
18133001.6%
 
17132401.6%
 
19130311.5%
 
20120331.4%
 
21109901.3%
 
22104911.2%
 
23100411.2%
 
Other values (151)28391733.5%
 
ValueCountFrequency (%) 
-4117< 0.1%
 
-4047< 0.1%
 
-3969< 0.1%
 
-3887< 0.1%
 
-3798< 0.1%
 
-36113< 0.1%
 
-35133< 0.1%
 
-34160< 0.1%
 
-33155< 0.1%
 
-32200< 0.1%
 
ValueCountFrequency (%) 
1431< 0.1%
 
1331< 0.1%
 
1321< 0.1%
 
13113< 0.1%
 
13011< 0.1%
 
1294< 0.1%
 
12812< 0.1%
 
12711< 0.1%
 
1268< 0.1%
 
1259< 0.1%
 

Interactions

2022-05-27T17:57:28.137129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:28.618092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:29.117028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:29.594819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:30.087968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:30.534804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:31.036000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:31.473621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:31.893914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:32.299905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:32.747000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:33.191676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:33.669670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:34.135617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:34.570530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:35.064739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:35.516182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:35.856825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:36.196651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:36.511029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:36.938231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:37.232634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:37.597679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:37.936650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:38.321257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:38.745340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:39.139678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:39.628661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:40.051159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:40.497464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:40.938328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:41.450248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:41.999653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:42.555614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:43.086941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:43.507034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:43.983530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:44.442064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:44.747275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:45.124297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:45.448258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:45.854896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:46.204710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:46.670597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:47.122587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:47.533135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:47.880616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:48.249371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:48.616837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:48.921595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:49.287536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:49.704209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:50.132127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:50.601937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:50.966615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:51.264302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:51.565017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:51.853861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:52.165795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:52.511866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:52.941194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:53.386695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:53.897085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:54.369733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:54.855871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:55.348328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:56.743827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:57.297225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:57.779211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:58.191074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:58.610109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:59.015409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:59.375858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:57:59.834112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:00.168788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:00.493921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:00.782773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:01.133936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:01.474022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:01.772089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:02.149693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:02.479234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:02.827539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:03.249712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:03.579178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:03.862603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:04.176438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:04.549637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:04.887232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:05.215210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:05.552758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:05.926570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:06.224622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:06.601329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:07.034311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:07.378218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:07.717626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:07.991270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:08.281742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:08.602616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:09.043016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:09.434077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:09.877224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:10.260763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:10.677526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:11.126912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:11.619944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:12.064606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:12.527241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:13.035068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:13.553478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:13.984400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:14.408351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:14.789050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:15.127717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:15.469732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:15.949491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:16.382485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:16.781538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:17.193131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:17.727638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:18.175657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:18.545368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:19.031358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:19.566053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:20.025613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:20.515450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:20.990801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:21.497698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:21.926080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:22.563963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:22.934283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:23.312675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:23.707851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:23.986729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:24.285018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:24.601745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:24.920813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:25.286077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:25.707600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:25.976869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:26.397417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:26.731699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:27.122342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:27.546429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:28.048925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:28.387821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:28.872483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:29.368584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:29.720552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:30.067950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:30.485287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:30.956252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:31.405735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:31.843731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:32.252667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:32.693771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:33.170024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:33.550637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:33.983366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:34.363750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:34.697641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:35.069362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:35.417935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:35.792937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:36.205778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:36.741485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:37.225504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:37.711718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:38.281254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:38.834045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:39.390156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:39.788262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:40.126351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:40.454486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:40.892744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:41.335216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:41.760461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:42.208993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:42.631508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:43.139959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:43.573170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:44.004014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:44.442573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:44.882541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:45.321965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:45.727075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:46.178514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:46.612933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:47.001213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:47.398988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:47.799156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:48.295823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:48.798867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:49.363858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:49.919074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:50.348161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:50.869769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:51.364526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:51.792823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:52.289211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:52.700697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:53.152528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:53.554212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:54.072819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:54.528951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:54.979233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:55.399935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:56.258357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:56.607963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:56.985967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:57.404743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:57.815529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:58.246484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:58.709962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:59.137102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:59.452472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:58:59.775604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:00.095295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:00.524766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:00.932468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:01.300356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:01.763475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:02.188919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:02.595928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:03.012236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:03.498337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:04.016331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:04.532976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:04.968780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:05.275822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:05.803766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:06.271130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:06.699120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:07.032340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:07.491186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:07.931635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:08.411126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:08.857392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:09.314941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:09.845743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:10.314403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:10.788095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:11.306896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:11.726455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:12.060366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:12.380939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:12.755843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:13.305984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:13.764425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:14.240843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:14.715166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:15.185944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:15.723401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:16.245535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:16.756215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-27T17:59:59.718678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-27T17:59:59.904234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-27T18:00:00.135443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-27T18:00:00.352975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-05-27T18:00:00.574156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-05-27T17:59:24.724447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:30.173973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:37.269604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-27T17:59:41.114486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTSUBS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFACTEFFHEAT_ARASD_VALTAX_VALSD1SD2TIFBASEACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDBLOCK_NUMLOT_NUMMARKET_AREA_CDREGIONAGE
08000008010001001987-08-01IQ0150000.0001WD1985-11-0119859 ANGEL LNODESSA335563.02.02.01.01.0565190.0174976.0384856.05358.0199620082617.0418262.0368262.0000000020165.058780211007.0U01271700000000001.111Northwest-9
19000008010001001985-11-01VQ0124000.0001WD1985-11-0119859 ANGEL LNODESSA335563.02.02.01.01.0565190.0174976.0384856.05358.0199620082617.0418262.0368262.0000000020165.058780211007.0U01271700000000001.111Northwest-11
211000009010001002021-10-27IQ01750000.0001WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.0197619981572.0453092.0453092.0000000019734.438490211007.0U01271700000000002.111Northwest45
314000009010001001997-05-01IQ01169900.0001WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.0197619981572.0453092.0453092.0000000019734.438490211007.0U01271700000000002.111Northwest21
420000010000001001988-06-01IQ0152500.0001WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.0192619732143.0173560.0123560.0000000019940.992559211007.0U01271700000000003.011Northwest62
521000010000001001983-02-01IQ0130000.0001WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.0192619732143.0173560.0123560.0000000019940.992559211007.0U01271700000000003.011Northwest57
622000010000101001998-08-01VQ0125000.0001WD1979-06-017020 COUNTY LINE RDUnincorporated33556-3.02.01.01.01.0379136.0100470.0250471.028195.0200120111919.0210422.0160422.0000000020021.362980211007.0U01271700000000004.111Northwest-3
723000010000201002012-06-19IQ02272500.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest25
824000010000201002004-06-01IQ01207500.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest17
925000010000201001999-02-01IQ01145000.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest12

Last rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTSUBS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFACTEFFHEAT_ARASD_VALTAX_VALSD1SD2TIFBASEACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDBLOCK_NUMLOT_NUMMARKET_AREA_CDREGIONAGE
8470922047201209435000001002000-01-10IQ0185000.05ELWD1971-01-01704 E DREW STPLANT CITY335633.02.01.01.01.0217427.075200.0139350.02877.0199020051195.0116985.0116985.0000000019710.367309221006.0P33282200000500006.021Northeast10
8470932047208209436000001002021-06-30IQ02169000.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast83
8470942047211209436000001002012-12-10IQ2A35700.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast74
8470952047213209436000001002006-02-28IQ02123900.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast68
8470962047214209436000001002005-09-07IQ0175000.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast67
8470972047220209436005001002006-10-25IQ01200000.05ELWD2005-08-23710 E DREW STPLANT CITY33563-66023.02.01.01.01.0196313.040400.0155546.0367.0200520131290.067435.025000.0000000020110.183654221006.0P33282200000500010.021Northeast1
8470982047222209436005001002005-08-23VQ0225000.05ELWD2005-08-23710 E DREW STPLANT CITY33563-66023.02.01.01.01.0196313.040400.0155546.0367.0200520131290.067435.025000.0000000020110.183654221006.0P33282200000500010.021Northeast0
8470992047223209436010001002015-08-21IQ2A134000.05ELWD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.0200620161290.099696.049696.0000000020160.288579221006.0P33282200000500011.021Northeast9
8471002047226209436010001002006-04-28IQ01195000.05ELWD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.0200620161290.099696.049696.0000000020160.288579221006.0P33282200000500011.021Northeast0
8471012047227209436010001002005-08-23VQ0225000.05ELWD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.0200620161290.099696.049696.0000000020160.288579221006.0P33282200000500011.021Northeast-1